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Polygraphic Recording Procedure for Measuring Sleep in Mice

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Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.

Aymen Zayed^1,2,3, Nidhameddine Belhadj⁴, Khaled Ben Khalifa^1,5

¹Technology and Medical Imaging Laboratory, Faculty of Medicine Monastir, University of Monastir, Monastir 5019, Tunisia.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

This study presents an electroencephalography (EEG)-based system for detecting drowsiness, achieving high accuracy. The developed drowsiness detection system enhances occupational safety by alerting individuals to prevent accidents.

Keywords:

EEG signals drowsiness detection feature selection machine learning

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Last Updated: Jun 21, 2025

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Recording Brain Activity with Ear-Electroencephalography

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Published on: March 31, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Drowsiness significantly contributes to accidents and defects across critical sectors like transportation and healthcare.
Current vigilance monitoring systems are insufficient, leading to preventable incidents.

Purpose of the Study:

To develop and evaluate an electroencephalography (EEG)-based system for accurate drowsiness detection.
To enhance occupational safety by providing timely alerts to individuals in a drowsy state.

Main Methods:

EEG signals were preprocessed, including artifact removal and segmentation into 10-second intervals.
Various machine learning algorithms (SVM, KNN, NB, DT, MLP) were employed for feature extraction and analysis.
The DROZY database, containing labeled awake and drowsy states, was utilized for model training and validation.

Main Results:

The proposed system achieved high accuracy rates of 99.84% in intra-subject mode and 96.4% in inter-subject mode.
Support Vector Machine (SVM) performed best for intra-subject drowsiness detection.
Multilayer Perceptron (MLP) demonstrated superior performance in inter-subject drowsiness detection.

Conclusions:

EEG-based drowsiness detection offers a promising approach for proactive safety measures.
The developed system can significantly reduce accident rates and improve vigilance in high-risk industries.
Machine learning models, particularly SVM and MLP, are effective for analyzing EEG signals for drowsiness detection.